Proto-OKN Theme 1: A Knowledge Graph Warehouse for Neighborhood Information

该项目旨在建立一个强大和可持续的数据基础设施，以整合社区一级的数据，协助和告知当地的各种利益攸关方。该项目将利用地方记录、人口普查数据和其他社区一级的数据，建立一个统一的数据库，以掌握社区一级各种信息来源之间的重要联系。项目成果包括用于构建和操作知识图谱仓库的集成社区级数据和软件。该项目的教育部分将把该项目的成果整合到课程内容中，促进学生指导，并促进教育创新，重点是相关STEM项目的包容性和多样性。该项目与国家司法研究所（NIJ）和其他专家实体合作，解决了在社区一级统一不同数据源的关键问题，例如，人口统计、土地使用、本地事件和伤害、创伤中心的邻近度等。建议的知识图仓库的目的是组织和维护相关的邻域级信息，通过零杆提取技术和关键短语生成方法的自由文本数据的数据转换。该数据库将支持高效查询和总结，其独特的结构具有适应性技术，包括用于趋势检测的新模式挖掘方法，确保可持续性和可扩展性，并与其他知识图兼容，并为新数据和实体类型进行增量更新和扩展。为确保数据的准确性，该项目计划整合来自各地方机构的数据，提供用户反馈机制，并维护可靠的元数据记录。为了减少偏见并提供全面的观点，该项目将不断更新新数据源的基础设施，通过元数据的可访问性和数据来源的记录来确保透明度。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

This project aims to establish a robust and sustainable data infrastructure to integrate neighborhood-level data to assist and inform various local stakeholders. Drawing on local records, census data and other neighborhood-level data the project will construct a unified database to capture crucial connections among the variety of neighborhood-level information sources. Project outcomes include integrated neighborhood-level data and software for constructing and operating a knowledge graph warehouse. The educational component of the project will integrate outcomes from this project into course content, foster student mentoring, and promote educational innovation with a focus on inclusivity and diversity within the associated STEM programs. Working in partnership with the National Institute of Justice (NIJ) and other expert entities, this project addresses critical issues in unifying disparate data sources at the neighborhood-level, e.g., demographics, land use, local incidents and injuries, proximity to trauma centers, and the like by leveraging advanced data extraction and record linkage methods. The proposed knowledge graph warehouse is designed to organize and maintain pertinent neighborhood-level information, with data transformation achieved through zero-shot extraction techniques and key-phrase generation methods for free text data. The warehouse will support efficient querying and summarization with adaptable techniques for its unique structure, including novel pattern mining methods for trend detection, ensuring sustainability and extensibility with compatibility for other knowledge graphs, and incorporating incremental updates and extensions for new data and entity types. To ensure data accuracy, the project plans to integrate data from various local agencies, provide user feedback mechanisms, and uphold a robust metadata record. In order to mitigate biases and to provide a comprehensive view, the project will continuously update the infrastructure with new data sources, ensuring transparency through accessibility of metadata and recording of data provenance.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.